Microsoft Teaches Autonomous Gliders to Make Decisions on the Fly

It was also a way of pushing the boundaries of the mathematical techniques that control a machine in a relatively safe but still very real environment. “With a glider, you can test these algorithms with minimal risk to people and property,” Mr. Kochenderfer said.

In building their algorithms, Mr. Kapoor and his team relied on techniques that date back decades – something called Markov decision processes. Essentially, this is a way of identifying and responding to uncertainty.

The approach is like the one you take when looking for change in a backpack crammed with random stuff. If you just stick your hand in the bag and start rummaging around, you face enormous uncertainty. You don’t know where to grab. But if, first, you remove the larger items like books and pencils that you know aren’t coins, the change falls to the bottom and the task gets easier. That is what Microsoft’s algorithms do – in a mathematical sense. They work to limit uncertainty, to reduce the scope of the problem.

Mr. Kapoor’s team included Andrey Kolobov, a researcher who specializes in these methods.

When he joined Microsoft’s research group four years ago, Mr. Kolobov fed these ideas into the company’s Windows operating system and its Bing search engine. Back then, he was dealing with uncertainty in the digital world. Now, he’s applying them in the physical world. “The number of applications where these methods are used is growing,” Mr. Kolobov said.

In the Nevada desert, the team launched its two gliders with help from a hand-held remote control. Once airborne, the gliders – or sailplanes – were left to their own devices. They were forced to fly with help from the wind and other air patterns.

Through those onboard algorithms, the gliders could analyze what was happening around them and then change directions as need be. They could learn from their environment, and although they could never be completely sure what would happen next, they could at least make educated guesses. Because it is dependent on phenomenon it has no control over, the glider must reason and plan in advance, Mr. Kolobov said.

The gliders planned their own paths to locations that could provide lift, and then they worked to exploit this lift, to ride those columns of rising air. When Mr. Kapoor pointed skyward from his Jeep, this is what happened. The math worked.

Still, these aircraft were far from perfect. Using a fiberglass glider with a 16-foot wingspan, the team hoped to set a record for autonomous flight time by a sailplane – more than five hours aloft. But after two days of trial and error, thanks to problems with radios and other equipment, that didn’t happen.

That researchers can improve on these sorts of learning algorithms has become an imperative to improving autonomous vehicles. To navigate the real world on their own, machines must mimic the way humans intuitively plan for their next action and deal with events they’ve never before experienced.

“The core problem for robotics is uncertainty,” said Ken Goldberg, a professor at University of California, Berkeley. “This is what differentiates robotics from a game like Go or chess.”

Over the last two years, researchers at DeepMind, a London-based artificial intelligence lab owned by Google, used neural networks and other techniques to build a system that could beat the world’s best players at Go, a game that is exponentially more difficult than chess. It was a milestone in the development of artificial intelligence. Now, researchers hope to reach bigger milestones here in the real world.

That’s the big reason Microsoft is building autonomous gliders. As Mr. Kolobov put it: “The A.I. systems of tomorrow will face all the same challenges.”


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